Exploration of the application potential of serum multi-biomarker model in colorectal cancer screening

Analyzing blood lipid and bile acid profile changes in colorectal cancer (CRC) patients. Evaluating the integrated model's diagnostic significance for CRC. Ninety-one individuals with colorectal cancer (CRC group) and 120 healthy volunteers (HC group) were selected for comparison. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3 were measured using immunoturbidimetric and colorimetric methods. Additionally, LC–MS/MS was employed to detect fifteen bile acids in the serum, along with six tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4. Group comparisons utilized independent sample t-tests and Mann–Whitney U tests. A binary logistic regression algorithm was applied to fit the indicators and establish a screening model; the diagnostic accuracy of individual Indicators and the model was analyzed using receiver operating characteristic (ROC) curves. The CRC group showed significantly lower levels in eight serum lipid indicators and eleven bile acids compared to the HC group (P < 0.05). Conversely, serum levels of TG, CA19-9, and CEA were elevated (P < 0.05). Among the measured parameters, ApoA2 stands out for its strong correlation with the presence of CRC, showcasing exceptional screening efficacy with an area under the curve (AUC) of 0.957, a sensitivity of 85.71%, and a specificity of 93.33%. The screening model, integrating ApoA1, ApoA2, lithocholic acid (LCA), and CEA, attained an impressive AUC of 0.995, surpassing the diagnostic accuracy of individual lipids, bile acids, and tumor markers. CRC patients manifest noteworthy alterations in both blood lipids and bile acid profiles. A screening model incorporating ApoA1, ApoA2, LCA, and CEA provides valuable insights for detecting CRC.

Gastroenterological Association and the Chinese Medical Association's Society of Gastroenterology advocate for a "two-step" screening approach for CRC 7,8 .The initial phase employs low-invasive screening techniques like hematological tests and multi-target stool DNA testing.Hematological tests, known for their simplicity, speed, safety, and minimally invasive, can capture evolving changes in CRC progression, proving valuable in screening and identifying high-risk individuals within ostensibly healthy populations.The subsequent stage entails a comprehensive colonoscopy, strategically optimizing colonoscopy resources and playing an important role in the early detection and intervention of CRC.
Recent studies reveal that alterations in blood lipids are a distinctive characteristic observed in various malignancies, including CRC patients 9 .Bile acids, metabolic products of cholesterol in the liver and processed in the intestine 10 , have been implicated in the risk of CRC due to disruptions in lipid and bile acid metabolism 11,12 .In this study, we assessed a panel of 9 serum lipids, encompassing total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3, alongside 15 serum bile acids and 6 tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4.This investigation involved 91 CRC patients and 120 healthy volunteers.The application of a data analysis model was employed to explore its value in identifying CRC.

Study subjects and enrollment criteria
Samples were collected from August 2022 to February 2023 at Renji Hospital affiliated with Shanghai Jiao Tong University School of Medicine.The study participants included 91 CRC patients, comprising the CRC group, and 120 healthy volunteers, forming the healthy control (HC) group.Inclusion criteria for the CRC group were as follows: patients newly diagnosed with primary CRC through histopathological examination, who had not undergone radiation, chemotherapy, or surgical treatment.All of the CRC patients were adenocarcinoma.Healthy volunteers were recruited from individuals undergoing medical check-ups at our center.After excluding colorectal cancer and other gastrointestinal diseases through colonoscopy, as well as excluding malignant tumors, inflammation, and cardiovascular diseases through thoracic CT scans, electrocardiograms, abdominal ultrasounds, and a comprehensive evaluation including fecal occult blood tests and blood examinations (such as routine blood tests, liver function, renal function, etc.), they were defined as healthy.From October 2023 to December 2023, we collected an additional 22 CRC patients and 22 healthy volunteers, matched based on the same inclusion criteria, to form an external validation set.There were no statistically significant differences in age and gender between the groups (P > 0.05).

Sample preprocessing
Four milliliters of fasting whole blood samples were collected using red top serum separator tube (Gongdong, China).These samples were centrifuged at 2685 × g for 10 min to separate the serum, with lipemic samples excluded.The serum was then stored at − 80 °C until analysis to avoid repeated freeze-thaw cycles.We utilized approximately one milliliter of serum for testing various indicators, strictly following the manufacturer's instructions throughout the testing process.

Detection of tumor markers
CEA, CA125, CA19-9, and CA72-4 were assayed using the Cobas e801 automatic analyzer (Roche, Switzerland), with corresponding reagent kits.CA50 and CA242 were analyzed using the Maglumi2000 automatic analyzer (Snibe, China), also with corresponding reagent kits.Chemiluminescence was the method of detection for all markers.

Statistical methods
Statistical analyses were performed using SPSS 17.0 and GraphPad 7.0.The Kolmogorov-Smirnov test was utilized for normality testing.Quantitative data with a normal distribution were reported as mean ± standard deviation ( x ± s) and compared using independent sample t-tests.Non-normally distributed quantitative data were presented as median (Q1, Q3) and analyzed using Mann-Whitney U test.A P-value less than 0.05 was considered statistically significant.
The indicators showing statistically significant differences between CRC group and HC group with AUC greater than 0.7 would be selected for developing a stepwise binary logistic regression model (Backward Likelihood Ratio method).We randomly divided participants' samples (by receiving date) such that 80% formed the training set and 20% formed the internal validation set.At each step, the indicator with the least contribution to the model's likelihood was removed.The process continues until the likelihood ratio test indicated that removing any further indicators would significantly change the model's fit (P-value < 0.05).The remaining indicators were considered the best subset, which composed the optimal model.The model's cut-off value was established at 0.5, where values exceeding 0.5 were categorized as CRC, while values below 0.5 were categorized as HC.Omnibus test was used to assesses the overall significance of the model.When the P-value of the Omnibus Test was less than 0.05, it indicated that at least one indicator was significant.Hosmer-Lemeshow test was used to assess the goodness-of-fit of the model.When the P-value of the Hosmer-Lemeshow test was greater than 0.05, it indicated model's classification forecasting were consistent with reality.The diagnostic accuracy of individual indicators and the model was assessed using receiver operating characteristic (ROC) curves.Flow diagram for development of the CRC screening model had been shown in Fig. 1.

Ethics declarations
This study received approval from the Ethics Committee of Renji Hospital Affiliated with Shanghai Jiao Tong University School of Medicine (case number RA-2022-335) and adhered to the principles outlined in the Declaration of Helsinki.Prior to their inclusion in the study, all patients provided informed consent.

Characteristics of the study populations
The median age of participants in the HC and CRC groups was 65 and 66 years old, respectively.Males comprised 64.17% and 63.73% of the HC and CRC groups, respectively.Early and middle-stage tumors (Stages I, II, and III) were prevalent in the CRC group, accounting for 87.91%.Late-stage tumors (Stage IV) represented 12.09% of the CRC cases.Additionally, the tumor location distribution revealed 42.86% in the rectum and 57.14% in the colon.As the Table 1 showed.

Comparison of 9 lipid indicators between HC and CRC groups
In the CRC group, the serum levels of TC, HDL-C, LDL-C, ApoA1, ApoA2, ApoB, ApoC2 and ApoC3 were significantly lower than those in the HC group (P < 0.05).The level of TG was higher in the CRC group compared to the HC group (P < 0.05), as shown in Table 2 and Fig. 2.

Comparison of 15 bile acids between HC and CRC groups
In the CRC group, the serum levels of free primary bile acids including CA, CDCA, as well as secondary bile acids such as DCA, GDCA, TDCA, LCA, GLCA, TLCA, UDCA, GUDCA, TUDCA, were significantly lower than those in the HC group (P < 0.05).However, there were no statistically significant differences in the levels of conjugated primary bile acids, including GCA, TCA, GCDCA, TCDCA between the two groups (P > 0.05), as shown in Table 2 and Fig. 3.

Comparison of 6 gastrointestinal tumor markers between HC and CRC groups
In the CRC group, the serum levels of CA19-9 and CEA were significantly higher than those in the HC group (P < 0.05).However, no statistically significant differences were observed in the levels of CA125, CA242, CA50, and CA72-4 between the two groups (P > 0.05), as shown in Table 2 and Fig. 4.

Establishment of the model
Eighty percent of study participants' samples were selected for forming the training set according to receiving date, including 96 cases from the HC group and 72 cases from the CRC group.Clinical characteristics of them were shown in Supplementary Table 4.The 11 indicators with AUC greater than 0.7 were included in a stepwise binary logistic regression analysis (Backward Likelihood Ratio method).When ApoA1 was removed in step 9, statistically significant difference was observed between model 8 and model 9(P = 0.024).Thus model 8 which composed of ApoA1, ApoA2, LCA, and CEA was considered as the final screening model.The model is represented as Y = 1/(1 + e −Logit(P) ) , where Logit (P) = − 4.847 × ApoA1 − 1.041 × ApoA2 − 0.132 × LCA + 2.2     4. The steps of developing the screening model were shown in Supplementary Table 5.

Analysis of model efficacy
The performance of Model Y was assessed using the ROC curve as a new variable.The AUC for diagnosing CRC was 0.995 (95% CI 0.969-0.999),as illustrated in Fig. 5.The model exhibited a sensitivity of 94.44%, a specificity of 97.92%, and an accuracy rate of 96.43%, as detailed in Table 4.The other 20% of study participants' samples (24 cases from the HC group and 19 cases from the CRC group) were utilized as internal validation set and assessed using Model Y.The results showed that 22 cases from the HC group and 18 cases from the CRC group were correctly classified, yielding a sensitivity of 94.74%, a specificity of 91.67%, and an overall accuracy rate of 93.02%.This accuracy rate was essentially consistent with that of the training set, as depicted in Fig. 6.

External validation of the model
For external validation of our findings, we recruited an additional 22 CRC patients and 22 healthy volunteers to form an independent validation set.Then evaluated Model Y's diagnostic accuracy on this external validation

Figure 1 .
Figure 1.Flow diagram for development of the CRC screening model.HC Healthy control, CRC Colorectal cancer.

Figure 5 .
Figure 5. ROC curve of the model and the markers that make up the model in screening CRC.

Figure 6 .
Figure 6.The performance of the model in screening CRC.HC Healthy control, CRC Colorectal cancer.

Table 1 .
Clinical characteristics of the participants.HC Healthy control, CRC Colorectal cancer.

Table 3 .
Performance of the indicators in screening CRC.CI Confidence interval, Optimal Cutoff Value: Cutoff value that maximizes the sum of sensitivity and specificity; Youden's Index: Sensitivity + Specificity − 1.